The University of Florida and Guana Tolomato Matanzas National Estuarine Research Reserve (GTMNERR) are partnering with the local community and broader science community to develop a web-based, public-facing, interactive dashboard to provide access to Guana Estuary datasets. The aim of this work is to support open science and to increase diverse engagement with the Guana Estuary within the GTMNERR by making the data available interactively, using visualization tools.
To this end, the project team sought feedback from those who have been involved with the Guana Estuary to help them to better understand their needs. This document summarizes the results of an online survey that was made available via email, social media, and QR code.
We received responses from 51 individuals. Out of these, 14 surveys were unfinished. For this report, we also took the unfinished surveys into account.
47 respondents filled in the survey based on a link received via email, 3 via social media, 0 via the QR code available at the GTMNERR Welcome Center, and 1 via the QR code available at the kiosk at the dam.
The survey started with asking respondents about their connection to the Guana Estuary, how often they engage with the Guana Estuary, what data they would be interested in, and whether or not they ever accessed data associated with the Guana Estuary.
For the purposes of this project, and this survey, “Guana Estuary” refers to the Guana Lake and Guana River: the area north and south of the Guana Dam, from Micklers Road to the Tolomato River / intracoastal.
We asked respondents about their connection with the Guana Estuary. The figure below summarizes the responses, but note that people could pick more than 1 option. This is why the sum of all percentages adds up to more than 100%.
In total there were 111 connections chosen. E.g. a little over 60% of respondents do recreational activities at the Guana Estuary, and almost 50% collect data or use data for scientific purposes - and these choices are not mutually exclusive! Someone could collect data and also enjoy the Guana Estuary recreationally. Or volunteer and also use the Guana Estuary for educational purposes.
Under “Other”, respondents answered:
We asked respondents what Guana Estuary data they would be interested in, regardless of whether or not they currently have access to these data. Respondents were also asked to rank these datasets, with 1 being the data they are most interested in. They could pick as many or few as they wanted.
The figure below shows the percentage of respondents that picked a particular dataset being of interest to them. E.g. over 80% of respondents picked water quality data. The colors indicate how they ranked it: for instance, almost 40% of all respondents ranked water quality data as their number 1 dataset of interest.
Under “Other”, the 4 types of datasets mentioned were: historical maps, water fowl, dam operations, natural resource management practices/techniques/results.
The survey asked respondents whether they had accessed data before, and by “data”, we meant “information, especially facts or numbers, collected to be examined and considered and used to help decision-making; or information in an electronic form that can be stored and used by a computer” for instance spreadsheets, databases, graphs, and maps.”
Based on their response whether or not they had accessed data, respondents answered different sets of questions. The results are summarized in the next two sections.
Take home message
For respondents that had not (yet) accessed data (N = 8), the figure below summarizes their answers from section 2.3 (datasets of interest). In this figure, the datasets are ordered according to their average ranking, once again 1 being the dataset of most interest.
This paints an interesting picture, as, for instance, water quality data were picked by most respondents, but in terms of average ranking it comes in 4th (3rd) place. Only one person responded they were not interested in any data (“None”), hence this item ranks first, as the average of 1 is 1… We can essentially disregard this item. The 3 datasets that score less than an average ranking of 3 are water level information, reserve or trail closures, and water quality information. However, information on vegetation, and information on fish, shellfish and other aquatic organisms was also picked by more than 60% (5 respondents) - but it was ranked lower on average.
These responses will still be linked to the answers in 2.3
The survey asked these respondents broad questions on how often they would access these data, and what they would use them for.
This figure shows that 25% (2 respondents) were not interested in accessing data, and that about half of the respondents would either access data either once a month or once a year (25% each).
In terms of what people would use data for, the majority would use it for (non-research / non-educational) work-related purposes and decision making, as per the figure below. Also here, respondents could pick more than one answer, so the sum of all percentages is more than 100%.
Under “Other”, respondents listed:
Take home message
For respondents that have/had accessed data before, the survey asked which datasets they had accessed, and a number of detailed questions about their experiences related to how they accessed these data, the advantages and disadvantages of this access, the frequency of access, the usage of the data, and respondents’ satisfaction with these data (for their needs).
The following table summarizes the detailed questions per dataset. The numbers represent percentages of respondents, or, in the case of multiple possible answers, percentages of all responses (indicated with an asterisk, ). This table still needs some reorganizing and reordering.*
There was an option “Other”, to which there was one response: LiDAR data. This information will be added to this table.
| q_text | Information on fish, shellfish or other aquatic organisms | Information on terrestrial animals | Information on vegetation (salt marsh or uplands) | Reserve or trail closures | Water level information (tides, Guana lake, river) | Water quality information (including nutrients and algae) | Weather information |
|---|---|---|---|---|---|---|---|
| How do you most frequently obtain or access these data? | |||||||
| Download from website (If so, what website?) | 35 | 57 | 36 | 56 | 30 | 26 | 75 |
| Other (please specify) | 10 | 14 | 21 | 11 | 30 | 19 | 25 |
| Request from a GTMNERR staff member by email | 55 | 29 | 43 | 22 | 40 | 55 | 0 |
| Pick-up paper copy in person | 0 | 0 | 0 | 11 | 0 | 0 | 0 |
| What are the advantages of this primary method of accessing or obtaining these data?* | |||||||
| Data is received quickly after request | 21 | 0 | 12 | 19 | 16 | 18 | 10 |
| Easy/convenient to access | 29 | 40 | 42 | 31 | 41 | 35 | 43 |
| Other (please specify) | 7 | 0 | 0 | 12 | 0 | 2 | 0 |
| Requesting the data is quick | 14 | 20 | 12 | 12 | 12 | 18 | 19 |
| The format the data are delivered / accessed in is useful | 26 | 30 | 21 | 12 | 25 | 24 | 19 |
| There are no advantages | 2 | 10 | 12 | 12 | 6 | 4 | 10 |
| What are the disadvantages of the primary method of accessing or obtaining these data?* | |||||||
| Difficult/Complicated to access | 13 | 10 | 12 | 10 | 10 | 8 | 19 |
| Other (please specify) | 17 | 30 | 18 | 30 | 19 | 25 | 25 |
| Slow to receive | 4 | 10 | 6 | 0 | 10 | 6 | 12 |
| The format the data are delivered / accessed in is not user-friendly | 13 | 0 | 18 | 0 | 14 | 11 | 6 |
| There are no disadvantages | 39 | 20 | 35 | 60 | 38 | 39 | 31 |
| Time consuming to request | 13 | 30 | 12 | 0 | 10 | 11 | 6 |
| How often do/did you access or obtain these data? | |||||||
| 2-3 times a month | 20 | 14 | 23 | 0 | 5 | 10 | 8 |
| Daily | 5 | 14 | 8 | 14 | 10 | 0 | 8 |
| Less than once a year | 15 | 0 | 15 | 0 | 20 | 17 | 0 |
| Once a month | 20 | 43 | 23 | 14 | 20 | 17 | 50 |
| Once every 6 months | 35 | 29 | 8 | 57 | 30 | 28 | 17 |
| Once every year | 5 | 0 | 23 | 14 | 0 | 24 | 8 |
| At least once a week | 0 | 0 | 0 | 0 | 15 | 3 | 8 |
| What do you typically use these data for?* | |||||||
| Decision making (for recreational/educational/scientific visits) | 9 | 20 | 13 | 27 | 18 | 12 | 17 |
| Educational purposes | 24 | 27 | 22 | 27 | 15 | 15 | 17 |
| Monitoring | 15 | 20 | 13 | 9 | 9 | 17 | 4 |
| Research | 38 | 13 | 26 | 18 | 33 | 35 | 26 |
| Work-related purposes (not research or education) | 15 | 13 | 17 | 9 | 18 | 17 | 26 |
| Other (please specify) | 0 | 7 | 9 | 9 | 6 | 4 | 9 |
| How well do these data generally satisfy your need(s)? | |||||||
| Extremely well | 5 | 14 | 0 | 14 | 5 | 3 | 8 |
| Moderately well | 45 | 43 | 46 | 29 | 55 | 41 | 25 |
| Slightly well | 15 | 29 | 0 | 0 | 15 | 14 | 25 |
| Very well | 35 | 14 | 54 | 57 | 25 | 41 | 42 |
The websites that respondents used to obtain data were:
“Other” avenues for accessing data were:
Disadvantages listed by respondents under “Other” were:
In terms of usage of data, respondents added the following under “Other”:
The survey asked respondents about their preferences regarding dashboard features (type and format of information, data delivery mode) and how they would access the dashboard.
By “dashboard” we meant a user interface on a computer display that presents (up-to-date) information with visualization tools such as graphs, charts, and tables - in a dynamic and interactive way.
Finally, the survey requested demographic information from respondents. This helps the project team get a better understanding of the dashboard’s target audience.
The project team will still refine details in this report, and distribute/publish the final version by the end of August 2023. The final report will also include dashboard design recommendations and considerations based on the survey results (and previous workshops).
To access the code that created this document, the survey result data, or jpg versions of the figures, go to https://github.com/GTMNERR-Science-Transfer/Survey-results.
The project team has also started drafting a basic dashboard; we will be in touch soon about further steps on this, and to inform you of upcoming participation and discussion opportunities.
Suggestions and comments on this draft report are very welcome; please email Dr. Geraldine Klarenberg at gklarenberg@ufl.edu, or leave an “Issue” on the above linked GitHub repository.
The figures below provide visual interpretations of the table in section 6, on the datasets that respondents have accessed.